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dc.contributor.author
Cappellin, Luca
dc.contributor.author
Aprea, Eugenio
dc.contributor.author
Granitto, Pablo Miguel
dc.contributor.author
Romano, Andrea
dc.contributor.author
Gasperi, Flavia
dc.contributor.author
Biasioli, Franco
dc.date.available
2015-12-22T20:36:11Z
dc.date.issued
2013-11
dc.identifier.citation
Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Romano, Andrea; Gasperi, Flavia; et al.; Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS; Elsevier; Food Research International; 54; 1; 11-2013; 1313-1320
dc.identifier.issn
0963-9969
dc.identifier.uri
http://hdl.handle.net/11336/3180
dc.description.abstract
Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest-Recursive Feature Elimination (RF-RFE) has been used to perform feature selection. In particular the most important GC-MS and PTR-MS variables related to gray mold susceptibility of the selected raspberry cultivars have been investigated. Moving from GC-MS profiling to the more rapid and less invasive PTR-MS fingerprinting leads to a cultivar characterization which is still related to the corresponding Botrytis susceptibility level and therefore marker identification is still possible.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Proton Transfer Reaction Mass Spectrometry
dc.subject
Raspberries
dc.subject
Cultivars
dc.subject
Chemometrics
dc.subject
Data Mining
dc.subject.classification
Alimentos y Bebidas
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-03-30 10:35:44.97925-03
dc.journal.volume
54
dc.journal.number
1
dc.journal.pagination
1313-1320
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Cappellin, Luca. Fondazione Edmund Mach. Research and Innovation Centre; Italia
dc.description.fil
Fil: Aprea, Eugenio. Fondazione Edmund Mach. Research and Innovation Centre; Italia
dc.description.fil
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
dc.description.fil
Fil: Romano, Andrea. Fondazione Edmund Mach. Research and Innovation Centre; Italia
dc.description.fil
Fil: Gasperi, Flavia. Fondazione Edmund Mach. Research and Innovation Centre; Italia
dc.description.fil
Fil: Biasioli, Franco. Fondazione Edmund Mach. Research and Innovation Centre; Italia
dc.journal.title
Food Research International
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0963996913000975
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.foodres.2013.02.004
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